Triple

T13581322
Position Surface form Disambiguated ID Type / Status
Subject Cole Hauser E324423 entity
Predicate name P16 FINISHED
Object Cole Hauser E324423 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Cole Hauser | Statement: [Cole Hauser, name, Cole Hauser]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cole Hauser
Context triple: [Cole Hauser, name, Cole Hauser]
  • A. Cole Hauser chosen
    Cole Hauser is an American actor known for his tough, authoritative roles in films like "Dazed and Confused," "Pitch Black," and "2 Fast 2 Furious," as well as the TV series "Yellowstone."
  • B. Dolph Lundgren
    Dolph Lundgren is a Swedish actor, director, and martial artist best known for his tough-guy roles in action films such as "Rocky IV" and "The Expendables" series.
  • C. Tim Matheson
    Tim Matheson is an American actor and director best known for his roles in films like "National Lampoon's Animal House" and numerous television series.
  • D. Jean-Claude Van Damme
    Jean-Claude Van Damme is a Belgian martial artist and action film star best known for movies like "Bloodsport," "Kickboxer," and "Universal Soldier."
  • E. Adam LaVorgna
    Adam LaVorgna is an American actor best known for his roles in the television series "7th Heaven" and films such as "The Beautician and the Beast."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d80769100c819099111274614f5ed2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb031e8048190a5f2ea934308036c completed April 12, 2026, 2:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f76bbf946c8190ba3d2b87cb11dc9d completed May 3, 2026, 3:37 p.m.
Created at: April 9, 2026, 9:48 p.m.